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Speech-to-Text: Recent Example

For a book project I recorded a 46 minute interview and had it transcribed by Speechmatics.com (as part of our testing for Lumberjack Builder). The interview was about 8600 words raw.

The good news is that it was over 99.98% accurate. I corrected 15 words out of a final 8100. The interview had good audio. I’m sure an audio perfectionist would have made it better, as would recording in a perfect environment, but this was pretty typical of most interview setups. It was recorded to a Zoom H1N as a WAV file. No compression.

Naturally, my off-mic questions and commentary was not transcribed accurately but it was never expected or intended to be. Although, to be fair, it was clear enough that a human transcriber would probably have got closer.

The less good news: my one female speaker was identified as about 15 different people! If I wanted a perfect transcript I probably would have cleaned up the punctuations as it wasn’t completely clean. But reality is that people do not speak in nice, neat sentences.

But neither the speaker identification nor the punctuation matter for the uses I’m going to make. I recognize that accurate punctuation would be needed for Closed (or open) Captioning for an output, but for production purposes perfect reproduction of the words is enough.

Multiple speakers will be handled in Builder’s Keyword Manager and reduced to one there. SpeedScriber has a feature to eliminate the speaker ID totally, which I would have used if a perfect output was my goal. For this project I simply eliminated any speaker ID.

The punctuation would also not be an issue in Builder, where we break on periods, but you can combine and break paragraphs with simple keystrokes. It’s not a problem for the book project as it will mostly be rewritten from spoken form to a more formal written style.

Most importantly for our needs, near perfect text is the perfect input for keyword, concept and emotion extraction.